Frauds, Posers And Sheep: A Virtue Theoretic Solution To The Acquaintance Debate
Bibliographic record
Abstract
The acquaintance debate in aesthetics has been traditionally divided between pessimists, who argue that testimony does not provide others with aesthetic knowledge of artworks, and optimists, who hold that acquaintance with an artwork is not a necessary precondition for acquiring aesthetic knowledge. In this paper I propose a reconciliationist solution to the acquaintance debate: while aesthetic knowledge can be had via testimony, aesthetic judgment requires acquaintance with the artwork. I develop this solution by situating it within a virtue aesthetics framework based on Ernest Sosa's virtue epistemology. I go on to apply the solution to the debates on moral testimony and expert testimony. An interesting variant on Gettier cases emerges: cases in which subjects have knowledge, but it has been formed by the wrong competence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".